Method to allow for question and answer system to dynamically return different responses based on roles

ABSTRACT

Embodiments are directed to a question and answer (QA) pipeline system that adjusts answers to input questions based on a user criteria, thus implementing a content-based determination of access permissions. The QA system allows for information to be retrieved based on permission granted to a user. Documents are ingested and assigned an access level based on a defined information access policy. The QA system is implemented with the defined information access policy, the ingested documents, and the inferred access levels. For the QA system implementation, a user enters a question; primary search and answer extraction stages are performed; candidate answer extraction is performed using only content the user is allowed to access; the candidate answers are scored, ranked, and merged; ranked answers based on user permissions are filtered; and answers are provided to the user.

BACKGROUND

Question and answer systems utilize the same corpus content for allusers when formulating answers. However, in many information accessscenarios, differential access to different users is desired. Forexample, differential access may be preferred for the followingsituations: if a user has paid for additional and/or high value content,when confidential information is shared within a company versusexternally-visible information, and when special information (e.g.,security clearance information, health care information, etc.) access isgranted to certain individuals. When differential access is assigned incurrent question and answer systems, access privileges are typicallyassigned at a file-space permissions level or by static user profilesmatched to document-level metadata such as tagging particular documentswith appropriate tags (e.g., “company confidential” tags). Such methodsare manually determined and are time consuming.

Thus, an improved question and answer system that allows fordifferential access for different users is desired.

SUMMARY

Embodiments are directed to a computer-implemented method, a computerprogram product, and a system for implementing a content-baseddetermination of access permissions.

In an embodiment, the computer-implemented method is implemented in asystem capable of answering questions, the system comprising a processorand a memory comprising instructions executed by the processor.

In an embodiment, the computer program product comprises a computerreadable storage medium having program instructions embodied therewith,the program instructions executable by a processor.

In an embodiment, the system comprises a processor and a memory, whichcomprises instructions executed by the processor.

In an embodiment, the processor executes the steps of: receiving dataindicative of an information access policy; performing documentingestion and access level classification of the ingested documentsbased on the information access policy; receiving a question from a userwith data indicative of a user permission level; performing search andanswer extraction to retrieve primary search retrieved content;performing candidate answer extraction using content the user is allowedto access based on the access level classification of the ingesteddocuments and the user permission level; and providing to the user anotification comprising the content the user is allowed to access.

Additional features and advantages are apparent from the followingdetailed description that proceeds with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following Figures:

FIG. 1 depicts a schematic diagram of an embodiment of a cognitivesystem implementing a question and answer (QA) generation system in acomputer network;

FIG. 2 illustrates a QA system pipeline, of a cognitive system, forprocessing an input question, according to an embodiment;

FIG. 3 is a flow diagram illustrating stages for adjusting answers toquestions based on a user criteria in a QA system, according to anembodiment;

FIG. 4 is a flowchart of a method for adjusting answers to questionsbased on a user criteria in a QA system, in accordance with anembodiment; and

FIG. 5 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented.

DETAILED DESCRIPTION

Embodiments are directed to content-based determination of accesspermissions for a question and answer system. By allowing for differentingested content to be used based on user privileges and/or usercriteria, flexibility is introduced into a question and answer system.Moreover, paid-content providers are able to incentivize customers toupgrade to higher paid service levels. According to embodiments herein,sensitive information in documents can be protected without the need tomanually tag more restrictive access levels by system administrators.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of,” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples are intendedto be non-limiting and are not exhaustive of the various possibilitiesfor implementing the mechanisms of the illustrative embodiments. It willbe apparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the example provided herein without departing from thespirit and scope of the present invention.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. IBMWatson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like accuracy at speeds far faster than human beings and on amuch larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding    -   Ingest and process vast amounts of structured and unstructured        data    -   Generate and evaluate hypotheses    -   Weigh and evaluate responses that are based only on relevant        evidence    -   Provide situation-specific advice, insights, and guidance    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes    -   Enable decision making at the point of impact (contextual        guidance)    -   Scale in proportion to the task    -   Extend and magnify human expertise and cognition    -   Identify resonating, human-like attributes and traits from        natural language    -   Deduce various language specific or agnostic attributes from        natural language    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall)    -   Predict and sense with situation awareness that mimics human        cognition based on experiences    -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system). The QA pipeline or system is anartificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input. Data storage devices store the corpusof data. A content creator creates content in a document for use as partof a corpus of data with the QA pipeline. The document may include anyfile, text, article, or source of data for use in the QA system. Forexample, a QA pipeline accesses a body of knowledge about the domain, orsubject matter area (e.g., financial domain, medical domain, legaldomain, etc.) where the body of knowledge (knowledgebase) can beorganized in a variety of configurations, e.g., a structured repositoryof domain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Content users input questions to the cognitive system which implementsthe QA pipeline. The QA pipeline then answers the input questions usingthe content in the corpus of data by evaluating documents, sections ofdocuments, portions of data in the corpus, or the like. When a processevaluates a given section of a document for semantic content, theprocess can use a variety of conventions to query such document from theQA pipeline, e.g., sending the query to the QA pipeline as a well-formedquestion which is then interpreted by the QA pipeline and a response isprovided containing one or more answers to the question. Semanticcontent is content based on the relation between signifiers, such aswords, phrases, signs, and symbols, and what they stand for, theirdenotation, or connotation. In other words, semantic content is contentthat interprets an expression, such as by using natural languageprocessing.

As will be described in greater detail hereafter, the QA pipelinereceives an input question, parses the question to extract the majorfeatures of the question, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the QA pipelinegenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA pipeline then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. There may behundreds or even thousands of reasoning algorithms applied, each ofwhich performs different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA pipeline. The statisticalmodel is used to summarize a level of confidence that the QA pipelinehas regarding the evidence that the potential response, i.e., candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA pipeline identifies candidateanswers that surface as being significantly stronger than others andthus generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA pipeline and mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e., candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QApipeline to more quickly and efficiently identity documents containingcontent related to a specific query. The content may also answer otherquestions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA pipeline. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA pipeline to identifyquestion and answer attributes of the content.

Operating on such content, the QA pipeline generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.,candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system 100 implementing a question and answer (QA) pipeline108 in a computer network 102. One example of a question/answergeneration operation which may be used in conjunction with theprinciples described herein is described in U.S. Patent ApplicationPublication No. 2011/0125734, which is herein incorporated by referencein its entirety. The cognitive system 100 is implemented on one or morecomputing devices 104 (comprising one or more processors and one or morememories, and potentially any other computing device elements generallyknown in the art including buses, storage devices, communicationinterfaces, and the like) connected to the computer network 102. Thenetwork 102 includes multiple computing devices 104 in communicationwith each other and with other devices or components via one or morewired and/or wireless data communication links, where each communicationlink comprises one or more of wires, routers, switches, transmitters,receivers, or the like. The cognitive system 100 and network 102 enablesquestion/answer (QA) generation functionality for one or more cognitivesystem users via their respective computing devices. Other embodimentsof the cognitive system 100 may be used with components, systems,sub-systems, and/or devices other than those that are depicted herein.

The cognitive system 100 is configured to implement a QA pipeline 108that receives inputs from various sources. For example, the cognitivesystem 100 receives input from the network 102, a corpus of electronicdocuments 140, cognitive system users, and/or other data and otherpossible sources of input. In one embodiment, some or all of the inputsto the cognitive system 100 are routed through the network 102. Thevarious computing devices 104 on the network 102 include access pointsfor content creators and QA system users. Some of the computing devices104 include devices for a database storing the corpus of data 140.Portions of the corpus of data 140 may also be provided on one or moreother network attached storage devices, in one or more databases, orother computing devices not explicitly shown in FIG. 1. The network 102includes local network connections and remote connections in variousembodiments, such that the cognitive system 100 may operate inenvironments of any size, including local and global, e.g., theInternet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 140 for use as part of a corpus of data with thecognitive system 100. The document includes any file, text, article, orsource of data for use in the cognitive system 100. QA system usersaccess the cognitive system 100 via a network connection or an Internetconnection to the network 102, and input questions to the cognitivesystem 100 that are answered by the content in the corpus of data 140.In one embodiment, the questions are formed using natural language. Thecognitive system 100 parses and interprets the question via a QApipeline 108, and provides a response to the cognitive system usercontaining one or more answers to the question. In some embodiments, thecognitive system 100 provides a response to users in a ranked list ofcandidate answers while in other illustrative embodiments, the cognitivesystem 100 provides a single final answer or a combination of a finalanswer and ranked listing of other candidate answers.

The cognitive system 100 implements the QA pipeline 108 which comprisesa plurality of stages for processing an input question and the corpus ofdata 140. The QA pipeline 108 generates answers for the input questionbased on the processing of the input question and the corpus of data140. The QA pipeline 108 is described in greater detail with regard toFIG. 2.

In some illustrative embodiments, the cognitive system 100 may be theIBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a QA pipeline of the IBM Watson™ cognitive systemreceives an input question, which it then parses to extract the majorfeatures of the question, and which in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question. TheQA pipeline of the IBM Watson™ cognitive system then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. The scoresobtained from the various reasoning algorithms are then weighted againsta statistical model that summarizes a level of confidence that the QApipeline of the IBM Watson™ cognitive system has regarding the evidencethat the potential response, i.e., candidate answer, is inferred by thequestion. This process is repeated for each of the candidate answers togenerate a ranked listing of candidate answers which may then bepresented to the user that submitted the input question, or from which afinal answer is selected and presented to the user. More informationabout the QA pipeline of the IBM Watson™ cognitive system may beobtained, for example, from the IBM Corporation website, IBM Redbooks,and the like. For example, information about the QA pipeline of the IBMWatson™ cognitive system can be found in Yuan et al., “Watson andHealthcare.” IBM developerWorks, 2011 and “The Era of Cognitive Systems:An Inside Look at IBM Watson and How it Works” by Rob High, IBMRedbooks, 2012.

As shown in FIG. 1, in accordance with some illustrative embodiments,the cognitive system 100 is further augmented, in accordance with themechanisms of the illustrative embodiments, to include logic implementedin specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware.

Results from the corpus 140 are stored in storage device 150 associatedwith either the cognitive system 100, where the storage device 150 maybe a memory, a hard disk based storage device, flash memory, solid statestorage device, or the like (hereafter assumed to be a “memory” within-memory representations of the acyclic graphs for purposes ofdescription).

FIG. 2 illustrates a QA system pipeline 108, of a cognitive system, forprocessing an input question. The QA system pipeline 108 of FIG. 2 maybe implemented, for example, as QA pipeline 108 of cognitive system 100in FIG. 1. It should be appreciated that the stages as shown in FIG. 2are implemented as one or more software engines, components, or thelike, which are configured with logic for implementing the functionalityattributed to the particular stage. Each stage is implemented using oneor more of such software engines, components or the like. The softwareengines, components, etc., are executed on one or more processors of oneor more data processing systems or devices and utilize or operate ondata stored in one or more data storage devices, memories, or the like,on one or more of the data processing systems. Additional stages may beprovided to implement the improved mechanism, or separate logic from thepipeline 108 may be provided for interfacing with the pipeline 108 andimplementing the improved functionality and operations of theillustrative embodiments provided herein.

As shown in FIG. 2, the QA pipeline 108 comprises a plurality of stages205-250 through which the cognitive system operates to analyze an inputquestion and generate a final response. According to embodiments herein,the QA pipeline 108 operates to adjust answers to input questions basedon a user criteria, thus implementing a content-based determination ofaccess permissions.

In an initial question input stage 205, the QA pipeline 108 receives aninput question that is presented in a natural language format. Accordingto an embodiment, the input question is inputted by a user with a userprofile that includes one or more of a responsibility role and apermission level. That is, a user inputs, via a user interface, an inputquestion for which the user wishes to obtain an answer, e.g., “Who areWashington's closest advisors?”

In response to receiving the input question, the next stage of the QApipeline 108, i.e., content access constraint criteria determinationstage 210, derives from the one or more of the responsibility role andthe permission level a content access constraint criteria for the inputquestion. The content access constraint criteria is utilized to restrictcontent based on the user's responsibility role and/or permission level.

The next stage of the QA pipeline 108, i.e., the question and topicanalysis stage 215, parses the input question using natural languageprocessing (NLP) techniques to extract major features from the inputquestion, and classify the major features according to types, e.g.,names, dates, or any of a plethora of other defined topics. For example,in the example question above, the term “who” may be associated with atopic for “persons” indicating that the identity of a person is beingsought, “Washington” may be identified as a proper name of a person withwhich the question is associated, “closest” may be identified as a wordindicative of proximity or relationship, and “advisors” may beindicative of a noun or other language topic.

In addition, the extracted major features include key words and phrasesclassified into question characteristics, such as the focus of thequestion, the lexical answer type (LAT) of the question, and the like.As referenced to herein, a lexical answer type (LAT) is a word in, or aword inferred from, the input question that indicates the type of theanswer, independent of assigning semantics to that word. For example, inthe question “What maneuver was invented in the 1500s to speed up thegame and involves two pieces of the same color?” the LAT is the string“maneuver.” The focus of a question is the part of the question that, ifreplaced by the answer, makes the question a standalone statement. Forexample, in the question “What drug has been shown to relieve thesymptoms of ADD with relatively few side effects?,” the focus is “Whatdrug” since this phrase can be replaced with the answer, e.g.,“Adderall,” to generate the sentence “Adderall has been shown to relievethe symptoms of ADD with relatively few side effects.” The focus often,but not always, contains the LAT. On the other hand, in many cases it isnot possible to infer a meaningful LAT from the focus.

Referring again to FIG. 2, the identified major features are then usedduring the question decomposition stage 220 to decompose the questioninto one or more queries that are applied to the corpora ofdata/information 255 in order to generate one or more hypotheses. Thequeries are generated in any known or later developed query language,such as the Structure Query Language (SQL), or the like. The queries areapplied to one or more databases storing information about theelectronic texts, documents, articles, websites, and the like, that makeup the corpora of data/information 255. That is, these various sourcesthemselves, different collections of sources, and the like, represent adifferent corpus 257 within the corpora 255. There may be differentcorpora 257 defined for different collections of documents based onvarious criteria depending upon the particular implementation. Forexample, different corpora may be established for different topics,subject matter categories, sources of information, or the like. As oneexample, a first corpus may be associated with healthcare documentswhile a second corpus may be associated with financial documents.Alternatively, one corpus may be documents published by the U.S.Department of Energy while another corpus may be IBM Redbooks documents.Any collection of content having some similar attribute may beconsidered to be a corpus 257 within the corpora 255.

The queries are applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data140 in FIG. 1. The queries are applied to the corpus of data/informationat hypothesis generation stage 225 to generate results identifyingpotential hypotheses for answering the input question, which can then beevaluated. That is, the application of the queries results in theextraction of portions of the corpus of data/information matching thecriteria of the particular query. These portions of the corpus are thenanalyzed and used, during the hypothesis generation stage 225, togenerate hypotheses for answering the input question. These hypothesesare also referred to herein as “candidate answers” for the inputquestion. For any input question, at this stage 225, there may behundreds of hypotheses or candidate answers generated that may need tobe evaluated.

At the next stage, content access constraint criteria application stage230, the responsibility role and/or the permission level associated withthe user of the input question is applied to the candidate answersgenerated at stage 225. That is, the determined content accessconstraint criteria is used to block or allow (i.e., filter) answers.According to an embodiment, for each candidate answer received, anaccess level is inferred. If a candidate answer is manually tagged witha particular access level, that particular access level is utilized.Deep NLP/feature extraction may be performed on each text segment toinfer the access level. In another embodiment, a rules-based policy orclassification-based policy is applied to determine an access level.Once the access levels for the candidate answers are determined, theyare compared to the user's content access constraint criteria. Answerswith an access level higher than that assigned to the user, asidentified by the content access constraint criteria, are removed fromthe pool of candidate answers. Similarly, if one or more particular textsegments (e.g., passage, subsection, paragraph, chapter, article, andthe like) of a candidate answer has an access level that conflicts withthat of the user, that one or more particular text segment may beextracted while a remaining portion of the candidate answer isavailable.

The QA pipeline 108, in stage 235, then performs a deep analysis andcomparison of the language of the input question and the language ofeach hypothesis or “candidate answer” filtered by the previous stage230, as well as performs evidence scoring to evaluate the likelihoodthat the particular hypothesis is a correct answer for the inputquestion. As described in FIG. 1, this involves using a plurality ofreasoning algorithms, each performing a separate type of analysis of thelanguage of the input question and/or content of the corpus thatprovides evidence in support of, or not in support of, the hypothesis.Each reasoning algorithm generates a score based on the analysis itperforms which indicates a measure of relevance of the individualportions of the corpus of data/information extracted by application ofthe queries as well as a measure of the correctness of the correspondinghypothesis, i.e., a measure of confidence in the hypothesis. There arevarious ways of generating such scores depending upon the particularanalysis being performed. In general, however, these algorithms look forparticular terms, phrases, or patterns of text that are indicative ofterms, phrases, or patterns of interest and determine a degree ofmatching with higher degrees of matching being given relatively higherscores than lower degrees of matching.

In an embodiment, the content access constraint criteria application maybe performed at this later stage. That is, the responsibility roleand/or the permission level associated with the user of the inputquestion is applied to the analyzed and scored answers generated atstage 235.

In the synthesis stage 240, the large number of scores generated by thevarious reasoning algorithms are synthesized into confidence scores orconfidence measures for the various hypotheses. This process involvesapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QApipeline 108 and/or dynamically updated. For example, the weights forscores generated by algorithms that identify exactly matching terms andsynonyms may be set relatively higher than other algorithms that areevaluating publication dates for evidence passages. The weightsthemselves may be specified by subject matter experts or learned throughmachine learning processes that evaluate the significance ofcharacteristics evidence passages and their relative importance tooverall candidate answer generation.

The weighted scores are processed in accordance with a statistical modelgenerated through training of the QA pipeline 108 that identifies amanner by which these scores may be combined to generate a confidencescore or measure for the individual hypotheses or candidate answers.This confidence score or measure summarizes the level of confidence thatthe QA pipeline 108 has about the evidence that the candidate answer isinferred by the input question, i.e., that the candidate answer is thecorrect answer for the input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 245 which compares the confidencescores and measures to each other, compares them against predeterminedthresholds, or performs any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe correct answer to the input question. The hypotheses/candidateanswers are ranked according to these comparisons to generate a rankedlisting of hypotheses/candidate answers (hereafter simply referred to as“candidate answers”). From the ranked listing of candidate answers, atstage 250, a final answer and confidence score, or final set ofcandidate answers and confidence scores, are generated and output to thesubmitter of the original input question via a graphical user interface(GUI) or other mechanism for outputting information.

According to an embodiment herein, ranked answers may be filtered basedon user permissions. For example, answers to a user may be showndepending on the user's permission level and/or responsibility role,e.g., show answers to user if user has access to all content or show acertain percentage of answers based on the user's permission leveland/or responsibility role.

According to an embodiment, along with the final set of candidateanswers and confidence scores, where the candidate answers or portionsthereof are restricted to reflect the access level of the user, evidenceof the adjusted answers may be provided to the user via the GUI or othermechanism. The evidence may include, for example, an indication ofaccess levels of the answers being provided. Moreover, areas wherecontent is redacted may be indicated, and teasers (i.e., a portion ofredacted content) may be shown.

In an embodiment, and with reference to FIG. 3, a flow diagram 300illustrates the stages for adjusting answers to questions based on auser criteria in a QA system, such as the QA system pipeline 108. First,the initial information access policy is defined at stage 310. Theinformation access policy refers to how content is marked and identifiedto be assigned an access level. In an embodiment, individual documentsmay be tagged with access levels; such manually-tagged permissions aretaken as supervised training examples for the QA system 108. In anotherembodiment, a rules engine may be utilized to record access policyrules. For example, a rule may specify that managers are allowed to viewemployee salaries of employees within their organization, or thatsalesman are allowed to view deals, terms, and project durations forcontracts established within their territory of responsibility. In yetanother embodiment, access rights may be described in terms ofindividual annotations or thresholds based on items that are output fromdeep NLP analysis of the documents. For example, free website membersmay view portions of documents with no more than N analysis statementsor no more than X geopolitical entities.

The second stage is document ingestion and inferred access levelclassification 320. At ingestion time, manually tagged documents aresent for deep NLP processing. A model characterizing documents for eachaccess level may be generated by extracting features frommanually-tagged documents. Features of the inferred access levelclassification process include, but are not limited to: particularentity types, relation types, and predicates with the document content;indicating words such as section headers, document titles, or othermetadata features; particular annotations produced by document analysisidentifying high-value content determined by the domain, such as, forexample, stock analysis, intelligence, insights, corporate productcomparisons, or market analysis.

Stage 330 comprises implementing the question-answer system (e.g., QAsystem pipeline 108), as described above with reference to FIG. 2, withthe defined information access policy, the ingested documents, and theinferred access level classifications. To summarize the QA systemimplementation: a user enters a question; primary search and answerextraction stages are performed to block or allow primary searchretrieved content; candidate answer extraction is performed using onlycontent the user is allowed to access; all content or only contentallowed to be accessed by the user is utilized to score, rank, and mergecandidate answers; ranked answers are filtered based on userpermissions; and answers plus evidence are sent to the user.

Stage 340 comprises the document lifecycle. As documents are modified(i.e., re-ingesting a particular document), the document may beerroneously marked as globally accessible. Thus, according to anembodiment herein, documents may be analyzed and, depending on theconfiguration, the access level may be updated or the document may beflagged for human review.

Stage 350 comprises analytics. According to an embodiment, the QA system108 is able to report the number of user queries that cannot be answeredbased on current access level. This information may be used to suggestor recommend a different number of access levels, for example. Inanother example of analytics, it may be useful in a ground truthcollection tool to indicate the access level of the users performing thepopulating of the content. The access levels of these users may bemodulated to include the corpus coverage they are able to annotate.

FIG. 4 is a flowchart 400 of a method for adjusting answers to questionsbased on a user criteria in a QA system, such as the QA system pipeline108, in accordance with an embodiment.

At 410, a question from a user is received. The user has a user profilecomprising one or more of a responsibility role and a permission level.In an embodiment, the permission level may be based on a subscription ofthe user; for example, a subscription to a particular magazine ornewspaper.

At 420, a content access constraint criteria is derived from the one ormore of the responsibility role and the permission level. In anembodiment, the content access constraint criteria controls access toembedded content in documents based on a passage classifier modelanalysis including accumulated discovery. In an embodiment, a firstresponsibility role for a first employee without managementresponsibility receives a more restrictive content access constraintcriteria than a second employee with management responsibility.

At 430, natural language processing (NLP) and deep analytic analysis isapplied to content restricted to the content access constraint criteriato form an answer to the question.

At 440, the user is provided with a notification comprising the answerto the question.

According to embodiments provided herein, access permissions aredynamically determined, thus allowing more flexibility of access ofcontent. The question and answer system and methods provided herein thatallow for differential access for different users have severaladvantages: less time is required from the corpus administrator tomanually tag documents; more levels of access permissions and/or avariety of policy controls are encouraged to be established to reflectan organization's desired information access; document access can bemodified over time as the document content changes; content control canbe partitioned at a finer grained level such as by paragraph, sentence,or individual facts; the restriction on a document that humanadministrators neglected to protect can be upgraded (such as a documentwith information that should be restricted to company-confidentiallevel, but which was not tagged that way by the document author).

An example use case is as follows: a publication displays different dataif a customer is currently a paid subscriber. A non-subscriber receivesa summary of the requested information, potentially with a teaser suchas “we have more answers available for gold level subscription,” and apaid subscriber receives detailed requested information. This providesan encouragement for more paid users to join. In another example, anemployee website returns information based on whether the employee is amanager or non-manager. In yet another example, an employee website fora government project contains confidential and non-confidential data.Users permitted to see confidential data are allowed to access data notpermitted for users granted this type of access. Of course, depending onthe use case, administrators may wish to allow or disallow inferreddocument access levels. In an example embodiment, the QA system 108 maybe configured to only UPGRADE (tighten) the document's access level andnever DOWNGRADE (loosen) access constraints.

In an example workflow for the question and answer system and methodsprovided herein that allow for differential access for different users,the system may validate whether the manually-assigned access level fitsthe document content. Additionally, the system may recommend a number ofaccess levels based on various factors, such as number of users, type oforganization, and type and amount of content (e.g., an organization mayneed five different sensitivity levels instead of the current three).Moreover, the system allows for a company to not manually tag everydocument; instead, a training corpus with levels may be provided, andcontent is automatically tagged. Documents that are desired to be sentto external customers may be analyzed so that confidential content isredacted or removed, and the remaining content can be sent externally.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a head disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network(LAN), a wide area network (WAN) and/or a wireless network. The networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as Java, Smalltalk, C++ or thelike, and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computer,or entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including LAN or WAN, or the connection may be made toan external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatuses(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operations steps to be performed on the computer,other programmable apparatus, or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical functions. In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 5 is a block diagram of an example data processing system 500 inwhich aspects of the illustrative embodiments are implemented. Dataprocessing system 500 is an example of a computer, such as a server orclient, in which computer usable code or instructions implementing theprocess for illustrative embodiments are located. In one embodiment,FIG. 5 represents a server computing device, such as a server, whichimplements the cognitive system 100 described herein.

In the depicted example, data processing system 500 can employ a hubarchitecture including a north bridge and memory controller hub (NB/MCH)501 and south bridge and input/output (I/O) controller hub (SB/ICH) 502.Processing unit 503, main memory 504, and graphics processor 505 can beconnected to the NB/MCH 501. Graphics processor 505 can be connected tothe NB/MCH 501 through, for example, an accelerated graphics port (AGP).

In the depicted example, a network adapter 506 connects to the SB/ICH502. An audio adapter 507, keyboard and mouse adapter 508, modem 509,read only memory (ROM) 505, hard disk drive (HDD) 511, optical drive(e.g., CD or DVD) 512, universal serial bus (USB) ports and othercommunication ports 513, and PCI/PCIe devices 514 may connect to theSB/ICH 502 through bus system 516. PCI/PCIe devices 514 may includeEthernet adapters, add-in cards, and PC cards for notebook computers.ROM 505 may be, for example, a flash basic input/output system (BIOS).The HDD 511 and optical drive 512 can use an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. A super I/O (SIO) device 515 can be connected to the SB/ICH502.

An operating system can run on processing unit 503. The operating systemcan coordinate and provide control of various components within the dataprocessing system 500. As a client, the operating system can be acommercially available operating system. An object-oriented programmingsystem, such as the Java programming system, may run in conjunction withthe operating system and provide calls to the operating system from theobject-oriented programs or applications executing on the dataprocessing system 500. As a server, the data processing system 500 canbe an IBM® eServer™ System p® running the Advanced Interactive Executiveoperating system or the Linux operating system. The data processingsystem 500 can be a symmetric multiprocessor (SMP) system that caninclude a plurality of processors in the processing unit 503.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as the HDD 511, and are loaded into the main memory 504 forexecution by the processing unit 503. The processes for embodiments ofthe question and answer system pipeline 108, described herein, can beperformed by the processing unit 503 using computer usable program code,which can be located in a memory such as, for example, main memory 504,ROM 505, or in one or more peripheral devices.

A bus system 516 can be comprised of one or more busses. The bus system516 can be implemented using any type of communication fabric orarchitecture that can provide for a transfer of data between differentcomponents or devices attached to the fabric or architecture. Acommunication unit such as the modem 509 or the network adapter 506 caninclude one or more devices that can be used to transmit and receivedata.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIG. 5 may vary depending on the implementation. Otherinternal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives may be used inaddition to or in place of the hardware depicted. Moreover, the dataprocessing system 500 can take the form of any of a number of differentdata processing systems, including but not limited to, client computingdevices, server computing devices, tablet computers, laptop computers,telephone or other communication devices, personal digital assistants,and the like. Essentially, data processing system 500 can be any knownor later developed data processing system without architecturallimitation.

The system and processes of the figures are not exclusive. Othersystems, processes, and menus may be derived in accordance with theprinciples of embodiments described herein to accomplish the sameobjectives. It is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the embodiments. Asdescribed herein, the various systems, subsystems, agents, managers, andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112(f) unless the elementis expressly recited using the phrase “means for.”

Although the invention has been described with reference to exemplaryembodiments, it is not limited thereto. Those skilled in the art willappreciate that numerous changes and modifications may be made to thepreferred embodiments of the invention and that such changes andmodifications may be made without departing from the true spirit of theinvention. It is therefore intended that the appended claims beconstrued to cover all such equivalent variations as fall within thetrue spirit and scope of the invention.

We claim:
 1. A computer implemented method for implementing acontent-based determination of access permissions by an informationhandling system capable of answering questions, the system comprising aprocessor and a memory comprising instructions executed by theprocessor, the method comprising: receiving data indicative of aninformation access policy; performing document ingestion and accesslevel classification of the ingested documents based on the informationaccess policy; receiving a question from a user with data indicative ofa user permission level; performing search and answer extraction toretrieve primary search retrieved content; performing candidate answerextraction using content the user is allowed to access based on theaccess level classification of the ingested documents and the userpermission level; and providing to the user a notification comprisingthe content the user is allowed to access.
 2. The method of claim 1,further comprising: utilizing all content or the content the user isallowed to access to score, rank, and merge candidate answers; andfiltering ranked answers based on the user permission level; wherein thenotification to the user further comprises the filtered, ranked answers.3. The method of claim 1, wherein the access level classification of theingested documents comprises one or more of manually tagged, extractedbased on natural language processing (NLP) and deep analytic analysis,based on a rules-based policy, or based on a classification-basedpolicy.
 4. The method of claim 1, wherein data indicative of aresponsibility role is associated with the user, and wherein thecandidate answer extraction is further based on the responsibility role.5. The method of claim 1, further comprising implementing a documentlifecycle process to update the access level classification of one ormore of the ingested documents.
 6. The method of claim 1, wherein thenotification to the user further comprises an indication of accesslevels of the content the user is allowed to access.
 7. The method ofclaim 6, wherein the notification to the user further comprises one ormore of an indication of areas where content is redacted and a portionof redacted content.
 8. A computer program product for implementing acontent-based determination of access permissions by an informationhandling system capable of answering questions, the computer programproduct comprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor to cause the processor to: receive data indicative of aninformation access policy; perform document ingestion and access levelclassification of the ingested documents based on the information accesspolicy; receive a question from a user with data indicative of a userpermission level; perform search and answer extraction to retrieveprimary search retrieved content; perform candidate answer extractionusing content the user is allowed to access based on the access levelclassification of the ingested documents and the user permission level;and provide to the user a notification comprising the content the useris allowed to access.
 9. The computer program product of claim 8,wherein the program instructions further cause the processor to: utilizeall content or the content the user is allowed to access to score, rank,and merge candidate answers; and filter ranked answers based on the userpermission level; wherein the notification to the user further comprisesthe filtered, ranked answers.
 10. The computer program product of claim8, wherein the access level classification of the ingested documentscomprises one or more of manually tagged, extracted based on naturallanguage processing (NLP) and deep analytic analysis, based on arules-based policy, or based on a classification-based policy.
 11. Thecomputer program product of claim 8, wherein data indicative of aresponsibility role is associated with the user, and wherein thecandidate answer extraction is further based on the responsibility role.12. The computer program product of claim 8, wherein the programinstructions further cause the processor to: implement a documentlifecycle process to update the access level classification of one ormore of the ingested documents.
 13. The computer program product ofclaim 8, wherein the notification to the user further comprises one ormore of an indication of access levels of the content the user isallowed to access; an indication of areas where content is redacted; anda portion of redacted content.
 14. A system for implementing acontent-based determination of access permissions, the systemcomprising: a memory comprising executable instructions; and a processorconfigured to execute the executable instructions to: receive dataindicative of an information access policy; perform document ingestionand access level classification of the ingested documents based on theinformation access policy; receive a question from a user with dataindicative of a user permission level; perform search and answerextraction to retrieve primary search retrieved content; performcandidate answer extraction using content the user is allowed to accessbased on the access level classification of the ingested documents andthe user permission level; and provide to the user a notificationcomprising the content the user is allowed to access.
 15. The system ofclaim 14, wherein the processor is further configured to: utilize allcontent or the content the user is allowed to access to score, rank, andmerge candidate answers; and filter ranked answers based on the userpermission level; wherein the notification to the user further comprisesthe filtered, ranked answers.
 16. The system of claim 14, wherein theaccess level classification of the ingested documents comprises one ormore of manually tagged, extracted based on natural language processing(NLP) and deep analytic analysis, based on a rules-based policy, orbased on a classification-based policy.
 17. The system of claim 14,wherein data indicative of a responsibility role is associated with theuser, and wherein the candidate answer extraction is further based onthe responsibility role
 18. The system of claim 14, wherein theprocessor is further configured to: implement a document lifecycleprocess to update the access level classification of one or more of theingested documents.
 19. The system of claim 14, wherein the notificationto the user further comprises an indication of access levels of thecontent the user is allowed to access.
 20. The system of claim 19,wherein the notification to the user further comprises one or more of anindication of areas where content is redacted and a portion of redactedcontent.